Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations3982
Missing cells0
Missing cells (%)0.0%
Duplicate rows88
Duplicate rows (%)2.2%
Total size in memory311.2 KiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Dataset has 88 (2.2%) duplicate rowsDuplicates
bedrooms is highly overall correlated with sqft_livingHigh correlation
grade is highly overall correlated with price and 1 other fieldsHigh correlation
price is highly overall correlated with grade and 4 other fieldsHigh correlation
sqft_basement is highly overall correlated with price and 1 other fieldsHigh correlation
sqft_living is highly overall correlated with bedrooms and 3 other fieldsHigh correlation
sqft_lot is highly overall correlated with priceHigh correlation
yr_built is highly overall correlated with priceHigh correlation
sqft_basement has 196 (4.9%) zeros Zeros

Reproduction

Analysis started2024-12-09 02:03:41.696608
Analysis finished2024-12-09 02:03:48.706459
Duration7.01 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

date
Real number (ℝ)

Distinct325
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160692.29
Minimum160102
Maximum161230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-12-08T20:03:48.776535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum160102
5-th percentile160201
Q1160427
median160707
Q3160928
95-th percentile161209
Maximum161230
Range1128
Interquartile range (IQR)501

Descriptive statistics

Standard deviation313.81388
Coefficient of variation (CV)0.001952887
Kurtosis-1.000792
Mean160692.29
Median Absolute Deviation (MAD)277.5
Skewness-0.019729271
Sum6.3987669 × 108
Variance98479.154
MonotonicityNot monotonic
2024-12-08T20:03:48.876482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160519 39
 
1.0%
160526 36
 
0.9%
160525 33
 
0.8%
160427 31
 
0.8%
160928 30
 
0.8%
160609 30
 
0.8%
160428 30
 
0.8%
160613 29
 
0.7%
160711 29
 
0.7%
160518 28
 
0.7%
Other values (315) 3667
92.1%
ValueCountFrequency (%)
160102 1
 
< 0.1%
160103 1
 
< 0.1%
160104 6
 
0.2%
160105 7
0.2%
160106 7
0.2%
160107 7
0.2%
160108 8
0.2%
160111 5
 
0.1%
160112 16
0.4%
160113 13
0.3%
ValueCountFrequency (%)
161230 7
 
0.2%
161229 11
0.3%
161228 11
0.3%
161227 11
0.3%
161226 1
 
< 0.1%
161223 5
 
0.1%
161222 11
0.3%
161221 14
0.4%
161220 18
0.5%
161219 11
0.3%

floors
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
1
2422 
2
1013 
1.5
329 
3
 
196
RR
 
13

Length

Max length3
Median length1
Mean length1.1730286
Min length1

Characters and Unicode

Total characters4671
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row1.5
5th row1.5

Common Values

ValueCountFrequency (%)
1 2422
60.8%
2 1013
25.4%
1.5 329
 
8.3%
3 196
 
4.9%
RR 13
 
0.3%
2.5 9
 
0.2%

Length

2024-12-08T20:03:48.976857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-08T20:03:49.074842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2422
60.8%
2 1013
25.4%
1.5 329
 
8.3%
3 196
 
4.9%
rr 13
 
0.3%
2.5 9
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 2751
58.9%
2 1022
 
21.9%
. 338
 
7.2%
5 338
 
7.2%
3 196
 
4.2%
R 26
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4671
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2751
58.9%
2 1022
 
21.9%
. 338
 
7.2%
5 338
 
7.2%
3 196
 
4.2%
R 26
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4671
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2751
58.9%
2 1022
 
21.9%
. 338
 
7.2%
5 338
 
7.2%
3 196
 
4.2%
R 26
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4671
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2751
58.9%
2 1022
 
21.9%
. 338
 
7.2%
5 338
 
7.2%
3 196
 
4.2%
R 26
 
0.6%

bedrooms
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.820442
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-12-08T20:03:49.156657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.66047413
Coefficient of variation (CV)0.23417398
Kurtosis1.1575987
Mean2.820442
Median Absolute Deviation (MAD)0
Skewness0.19148611
Sum11231
Variance0.43622608
MonotonicityNot monotonic
2024-12-08T20:03:49.240228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2423
60.8%
2 1072
26.9%
4 395
 
9.9%
1 57
 
1.4%
5 29
 
0.7%
6 6
 
0.2%
ValueCountFrequency (%)
1 57
 
1.4%
2 1072
26.9%
3 2423
60.8%
4 395
 
9.9%
5 29
 
0.7%
6 6
 
0.2%
ValueCountFrequency (%)
6 6
 
0.2%
5 29
 
0.7%
4 395
 
9.9%
3 2423
60.8%
2 1072
26.9%
1 57
 
1.4%

sqft_living
Real number (ℝ)

High correlation 

Distinct1420
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1461.3546
Minimum520
Maximum7391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-12-08T20:03:49.326712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile816
Q11064
median1327.5
Q31704
95-th percentile2509
Maximum7391
Range6871
Interquartile range (IQR)640

Descriptive statistics

Standard deviation580.01113
Coefficient of variation (CV)0.39689965
Kurtosis10.319984
Mean1461.3546
Median Absolute Deviation (MAD)304
Skewness2.1599112
Sum5819114
Variance336412.91
MonotonicityNot monotonic
2024-12-08T20:03:49.426524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
936 33
 
0.8%
864 28
 
0.7%
768 28
 
0.7%
1028 28
 
0.7%
816 27
 
0.7%
1080 27
 
0.7%
1008 27
 
0.7%
720 26
 
0.7%
825 26
 
0.7%
775 26
 
0.7%
Other values (1410) 3706
93.1%
ValueCountFrequency (%)
520 1
 
< 0.1%
522 1
 
< 0.1%
572 1
 
< 0.1%
576 1
 
< 0.1%
580 1
 
< 0.1%
588 1
 
< 0.1%
608 1
 
< 0.1%
621 5
0.1%
624 3
0.1%
633 1
 
< 0.1%
ValueCountFrequency (%)
7391 1
< 0.1%
7071 1
< 0.1%
5952 1
< 0.1%
5785 1
< 0.1%
5546 2
0.1%
5169 1
< 0.1%
5026 1
< 0.1%
4815 1
< 0.1%
4689 1
< 0.1%
4464 1
< 0.1%

sqft_lot
Real number (ℝ)

High correlation 

Distinct174
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16552.039
Minimum1307
Maximum892980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-12-08T20:03:49.536590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1307
5-th percentile5227
Q16970
median8276
Q310454
95-th percentile21780
Maximum892980
Range891673
Interquartile range (IQR)3484

Descriptive statistics

Standard deviation52778.972
Coefficient of variation (CV)3.188669
Kurtosis136.92637
Mean16552.039
Median Absolute Deviation (MAD)1742
Skewness10.469048
Sum65910218
Variance2.7856199 × 109
MonotonicityNot monotonic
2024-12-08T20:03:49.806699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6970 395
 
9.9%
6534 319
 
8.0%
7405 305
 
7.7%
9148 287
 
7.2%
8276 281
 
7.1%
6098 257
 
6.5%
7841 256
 
6.4%
8712 244
 
6.1%
9583 187
 
4.7%
10454 156
 
3.9%
Other values (164) 1295
32.5%
ValueCountFrequency (%)
1307 2
 
0.1%
1742 5
 
0.1%
2178 7
 
0.2%
2614 8
 
0.2%
3049 14
 
0.4%
3485 23
0.6%
3920 14
 
0.4%
4356 43
1.1%
4792 37
0.9%
5227 50
1.3%
ValueCountFrequency (%)
892980 1
< 0.1%
876863 1
< 0.1%
872942 2
0.1%
871636 1
< 0.1%
832867 1
< 0.1%
811523 1
< 0.1%
586753 1
< 0.1%
568022 1
< 0.1%
513137 1
< 0.1%
426888 1
< 0.1%

sqft_basement
Real number (ℝ)

High correlation  Zeros 

Distinct1175
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1045.0068
Minimum0
Maximum4635
Zeros196
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-12-08T20:03:49.906769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201
Q1763
median1020
Q31324
95-th percentile1828
Maximum4635
Range4635
Interquartile range (IQR)561

Descriptive statistics

Standard deviation480.14676
Coefficient of variation (CV)0.4594676
Kurtosis1.5825232
Mean1045.0068
Median Absolute Deviation (MAD)279
Skewness0.37409712
Sum4161217
Variance230540.91
MonotonicityNot monotonic
2024-12-08T20:03:50.006851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 196
 
4.9%
720 52
 
1.3%
768 51
 
1.3%
936 50
 
1.3%
864 44
 
1.1%
528 42
 
1.1%
672 41
 
1.0%
480 36
 
0.9%
816 36
 
0.9%
432 32
 
0.8%
Other values (1165) 3402
85.4%
ValueCountFrequency (%)
0 196
4.9%
176 1
 
< 0.1%
180 1
 
< 0.1%
200 2
 
0.1%
220 1
 
< 0.1%
250 1
 
< 0.1%
268 1
 
< 0.1%
286 2
 
0.1%
288 1
 
< 0.1%
298 1
 
< 0.1%
ValueCountFrequency (%)
4635 1
< 0.1%
3807 1
< 0.1%
3040 1
< 0.1%
2963 1
< 0.1%
2931 1
< 0.1%
2914 1
< 0.1%
2807 2
0.1%
2799 1
< 0.1%
2753 1
< 0.1%
2676 1
< 0.1%

yr_built
Real number (ℝ)

High correlation 

Distinct117
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1977.1135
Minimum1850
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-12-08T20:03:50.106619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1850
5-th percentile1916.05
Q11955
median1981
Q32004
95-th percentile2016
Maximum2021
Range171
Interquartile range (IQR)49

Descriptive statistics

Standard deviation31.770239
Coefficient of variation (CV)0.016069001
Kurtosis-0.45641391
Mean1977.1135
Median Absolute Deviation (MAD)24
Skewness-0.65757831
Sum7872866
Variance1009.3481
MonotonicityNot monotonic
2024-12-08T20:03:50.211506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2016 266
 
6.7%
2015 239
 
6.0%
2004 104
 
2.6%
1950 93
 
2.3%
2003 90
 
2.3%
1900 89
 
2.2%
1952 83
 
2.1%
1955 81
 
2.0%
1920 78
 
2.0%
2006 76
 
1.9%
Other values (107) 2783
69.9%
ValueCountFrequency (%)
1850 1
 
< 0.1%
1880 1
 
< 0.1%
1889 1
 
< 0.1%
1900 89
2.2%
1904 1
 
< 0.1%
1905 7
 
0.2%
1907 3
 
0.1%
1908 4
 
0.1%
1909 4
 
0.1%
1910 52
1.3%
ValueCountFrequency (%)
2021 4
 
0.1%
2020 1
 
< 0.1%
2016 266
6.7%
2015 239
6.0%
2014 40
 
1.0%
2013 36
 
0.9%
2012 32
 
0.8%
2011 23
 
0.6%
2010 31
 
0.8%
2009 31
 
0.8%

condition
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68311569
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-12-08T20:03:50.296591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q10.66666667
median0.66666667
Q30.66666667
95-th percentile0.83333333
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.082617
Coefficient of variation (CV)0.12094145
Kurtosis4.0473958
Mean0.68311569
Median Absolute Deviation (MAD)0
Skewness-0.19077285
Sum2720.1667
Variance0.0068255687
MonotonicityNot monotonic
2024-12-08T20:03:50.376875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.666666667 3064
76.9%
0.833333333 660
 
16.6%
0.5 234
 
5.9%
0.333333333 15
 
0.4%
1 5
 
0.1%
0.166666667 3
 
0.1%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.166666667 3
 
0.1%
0.333333333 15
 
0.4%
0.5 234
 
5.9%
0.666666667 3064
76.9%
0.833333333 660
 
16.6%
1 5
 
0.1%
ValueCountFrequency (%)
1 5
 
0.1%
0.833333333 660
 
16.6%
0.666666667 3064
76.9%
0.5 234
 
5.9%
0.333333333 15
 
0.4%
0.166666667 3
 
0.1%
0 1
 
< 0.1%

grade
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38302361
Minimum0
Maximum1
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-12-08T20:03:50.456621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.4
median0.4
Q30.4
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.097664765
Coefficient of variation (CV)0.25498367
Kurtosis3.2489524
Mean0.38302361
Median Absolute Deviation (MAD)0
Skewness-0.176603
Sum1525.2
Variance0.0095384064
MonotonicityNot monotonic
2024-12-08T20:03:50.531905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.4 3110
78.1%
0.2 586
 
14.7%
0.6 254
 
6.4%
0 18
 
0.5%
0.8 12
 
0.3%
1 2
 
0.1%
ValueCountFrequency (%)
0 18
 
0.5%
0.2 586
 
14.7%
0.4 3110
78.1%
0.6 254
 
6.4%
0.8 12
 
0.3%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
0.8 12
 
0.3%
0.6 254
 
6.4%
0.4 3110
78.1%
0.2 586
 
14.7%
0 18
 
0.5%

price
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209741.18
Minimum29000
Maximum2122450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-12-08T20:03:50.626864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum29000
5-th percentile93000
Q1135000
median179000
Q3257375
95-th percentile415000
Maximum2122450
Range2093450
Interquartile range (IQR)122375

Descriptive statistics

Standard deviation113511.24
Coefficient of variation (CV)0.54119673
Kurtosis25.319319
Mean209741.18
Median Absolute Deviation (MAD)53500
Skewness2.9413621
Sum8.3518937 × 108
Variance1.2884802 × 1010
MonotonicityNot monotonic
2024-12-08T20:03:50.726468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115000 43
 
1.1%
140000 38
 
1.0%
145000 38
 
1.0%
175000 37
 
0.9%
160000 37
 
0.9%
135000 36
 
0.9%
130000 34
 
0.9%
110000 31
 
0.8%
150000 29
 
0.7%
185000 29
 
0.7%
Other values (1339) 3630
91.2%
ValueCountFrequency (%)
29000 1
< 0.1%
32000 1
< 0.1%
35000 1
< 0.1%
38000 1
< 0.1%
44000 1
< 0.1%
45000 1
< 0.1%
45150 1
< 0.1%
45550 1
< 0.1%
47000 1
< 0.1%
47800 1
< 0.1%
ValueCountFrequency (%)
2122450 1
< 0.1%
1000000 2
0.1%
925000 1
< 0.1%
910000 1
< 0.1%
900000 1
< 0.1%
887000 1
< 0.1%
842500 1
< 0.1%
825000 1
< 0.1%
800000 1
< 0.1%
792607.65 1
< 0.1%

Interactions

2024-12-08T20:03:47.821461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:41.967770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.646781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.306350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.035062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.706581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.596508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.376637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.068191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.896772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.045414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.726694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.388933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.114214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.776737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.683875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.456580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.154637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.970908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.120171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.791921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.476400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.189000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.854817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.756603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.526702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.248821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:48.046598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.209569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.867798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.556695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.267015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.089699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.852737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.606586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.358754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:48.121087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.285092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.936319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.646363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.342295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.166517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.931525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.685184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.426516image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:48.196470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.356462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.006686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.721674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.418384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.246572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.016816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.754684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.501791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:48.276800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.438025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.088472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.802224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.496777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.345462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.110501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.841455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.586351image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:48.346553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.506810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.156377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.879675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.569323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.429456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.192253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.916492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.668542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:48.419816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:42.576884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.236727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:43.962052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:44.638123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:45.516580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.276608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:46.987933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:47.748742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-08T20:03:50.800850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
bedroomsconditiondatefloorsgradepricesqft_basementsqft_livingsqft_lotyr_built
bedrooms1.0000.015-0.0010.2610.3570.3220.0850.5050.2020.171
condition0.0151.0000.0280.0300.0170.081-0.0100.0180.017-0.087
date-0.0010.0281.0000.0210.0080.0450.0250.0190.0310.020
floors0.2610.0300.0211.0000.2010.1430.2420.3190.1130.207
grade0.3570.0170.0080.2011.0000.5900.4500.6050.3060.294
price0.3220.0810.0450.1430.5901.0000.6970.7970.5180.705
sqft_basement0.085-0.0100.0250.2420.4500.6971.0000.5310.4110.495
sqft_living0.5050.0180.0190.3190.6050.7970.5311.0000.4700.406
sqft_lot0.2020.0170.0310.1130.3060.5180.4110.4701.0000.266
yr_built0.171-0.0870.0200.2070.2940.7050.4950.4060.2661.000

Missing values

2024-12-08T20:03:48.518461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-08T20:03:48.646500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datefloorsbedroomssqft_livingsqft_lotsqft_basementyr_builtconditiongradeprice
016050524739156628463519950.6666671.02122450.0
116110732323180150293119660.6666670.4385000.0
216102225398016988235620090.8333330.6728000.0
31610181.543557876863205020000.8333330.6842500.0
41609221.533485170320275320130.6666670.6925000.0
516061512202012632202019940.8333330.6431000.0
616062712244720909239620030.8333330.6680000.0
716100713118913939118920100.6666670.4228000.0
816041433279212197148219660.6666670.4285000.0
91603212422848276100220130.6666670.4279900.0
datefloorsbedroomssqft_livingsqft_lotsqft_basementyr_builtconditiongradeprice
39721609121311626098114620060.6666670.4175000.0
39731611301311406098110020090.6666670.4170000.0
39741603161312006534117620150.6666670.4186500.0
39751612131311546098106019950.6666670.4170000.0
39761602101.531273697085319940.6666670.4159000.0
39771601051311536970115319950.6666670.4145200.0
3978160311231464435681219970.6666670.4169500.0
397916092912999261498120010.6666670.4127500.0
398016040812867522786720020.6666670.2142900.0
39811605041312728276127220140.6666670.4196500.0

Duplicate rows

Most frequently occurring

datefloorsbedroomssqft_livingsqft_lotsqft_basementyr_builtconditiongradeprice# duplicates
29160502129881393998819100.6666670.485000.04
3416051212925827692519590.6666670.4119900.04
4916071112144874052019000.8333330.2154900.04
7116101012155210890127519660.8333330.4199000.04
81161108112616402059162019780.5000000.2315000.04
82161108132616402059162020210.8333330.2315000.04
1916032512966566393619330.5000000.4111000.03
23160407131617107593160820040.6666670.4275000.03
241604131310921393966019000.5000000.489900.03
2516041813112712197112719580.6666670.4135000.03